The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed from modern machines such as Medical Resonance (MR) imaging scanners, Computed Tomographic (CT) scanners and Positron Emission Tomographic (PET) scanners, to multimodality imaging systems such as PET-CT and PET-MRI systems. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2D”) image made of pixel elements, a three-dimensional (“3D”) image made of volume elements (“voxels”) or a four-dimensional (“4D”) image made of dynamic elements (“doxels”). Such 2D, 3D or 4D images are processed using medical image recognition techniques to determine the presence of anatomical abnormalities such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images, localize and segment anatomical structures, including possible abnormalities (or candidates), for further review. Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor in order to reach an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Many CAD or treatment applications would benefit from automatic and precise segmentation of articulated bones in CT images. FIG. 1 shows surface models (102 and 104) of an unfolded hand and folded hand respectively. The human hand anatomy typically includes 19 bones. Automatically segmenting hand bones remains a challenging task. This is largely due to the highly articulated nature of hand bones, which limits the effectiveness of registration-based segmentation methods. The use of low-level information, such as edge and grey level information derived from the target image (i.e., the image to be segmented) alone is insufficient for segmenting bones or distinguishing different bones that are in close proximity to each other.